Abstract
We present a method for 3D pose estimation of human motion in generative framework. For the generalization of application scenario, the observation information we utilized comes from monocular silhouettes. We distill prior information of human motion by performing conventional PCA on single motion capture data sequence. In doing so, the aims for both reducing dimensionality and extracting the prior knowledge of human motion are achieved simultaneously. We adopt the shape contexts descriptor to construct the matching function, by which the validity and the robustness of the matching between image features and synthesized model features can be ensured. To explore the solution space efficiently, we design the Annealed Genetic Algorithm (AGA) and Hierarchical Annealed Genetic Algorithm (HAGA) that searches the optimal solutions effectively by utilizing the characteristics of state space. Results of pose estimation on different motion sequences demonstrate that the novel generative method can achieves viewpoint invariant 3D pose estimation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3d human motion estimation. In: Proc. Conf. Computer Vision and Pattern Recognition, pp. 217–323 (2005)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proceedings of the 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 126–133 (2000)
Sidenbladh, H., Black, M., Fleet, D.: Stochastic tracking of 3D human figures using 2D image motion. In: European Conference on Computer Vision, vol. 2, pp. 702–718 (2000)
Sminchisescu, C., Triggs, B.: Covariance scaled sampling for monocular 3D body tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 447–454 (2001)
Agarwal, A., Triggs, B.: Tracking articulated motion using a mixture of autoregressive models. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 54–65. Springer, Heidelberg (2004)
Ning, H., Tan, T., Wang, L., Hu, W.: People tracking based on motion model and motion constraints withautomatic initialization. Pattern Recognition 37(7), 1423–1440 (2004)
Mori, G., Malik, J.: Recovering 3 D Human Body Configurations Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1052–1062 (2006)
Sidenbladh, H., Black, M., Sigal, L.: Implicit Probabilistic Models of Human Motion for Synthesis and Tracking. In: European Conference on Computer Vision, vol. 1, pp. 784–800 (2002)
Urtasun, R., Fleet, D., Fua, P.: Monocular 3-D Tracking of the Golf Swing. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE Computer Society Press, Los Alamitos (2005)
Michalewicz, Z.: Genetic algorithms+ data structures= evolution programs. Springer, Heidelberg (1996)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Aggarwal, J., Cai, Q.: Human motion analysis: A review. Computer Vision and Image Understanding 73(3), 428–440 (1999)
Gavrila, D.: Visual analysis of human movement: A survey. Computer Vision and Image Understanding 73(1), 82–98 (1999)
Moeslund, T., Granum, E.: A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 81(3), 231–268 (2001)
Urtasun, R., Fua, P.: 3D Human Body Tracking using Deterministic Temporal Motion Models. In: European Conference on Computer Vision, vol. 3, pp. 92–106 (2004)
Arulampalam, M., Maskell, S., Gordon, N., Clapp, T., Sci, D., Organ, T., Adelaide, S.: A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking. IEEE Transactions on Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing 50(2), 174–188 (2002)
Deutscher, J., Davison, A., Reid, I.: Automatic partitioning of high dimensional search spaces associated with articulated body motion capture. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2001)
Jin, X., Tai, C.: Convolution surfaces for arcs and quadratic curves with a varying kernel. The Visual Computer 18(8), 530–546 (2002)
Sigal, L., Black, M.J.: Humaneva: Synchronized video and motion capture dataset for evaluation of articulated human motion. Technical Report CS-06-08, Brown University (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, X., Liu, Y. (2007). Generative Estimation of 3D Human Pose Using Shape Contexts Matching. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-76386-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
Online ISBN: 978-3-540-76386-4
eBook Packages: Computer ScienceComputer Science (R0)